Covid Report for Family

Background

Notes and format last updated May 7, 2020

Starting on the May 7th update, the NY Times began including probable covid cases/deaths along with confirmed. This mostly affects death counts – for certain geographies that include probable COVID deaths in addition to confirmed, these are now added to the totals. For the time being, they were all added to the May 6th totals, causing a big spike at the U.S. level. Over time, NY Times will revise their historical counts and distribute these added deaths when they actually occurred, so the spike should fade.

• This page shows COVID data broken down into regions that affect our family. It pulls from publicly available raw data from the NY Times, which is reported daily. It’s on a one-day lag, so when you see this report, the most recent day included will be that reported the day prior.
• The table of contents above summarizes the major sections and can be used to jump to those sections.
• Each major section is then divided into tabs to allow you to more easily shuffle between geographies and metrics.
  • If you open on your phone, I don’t believe the tabs will work, so you’ll just see the page as one long, continuous document.
• More detailed descriptions and context can be found in each section.

Growth rates

Heat maps

• The two heat maps below compare how quickly total cases or deaths have grown at various times in our respective geopgraphies.
• The first plot compares growth rate for total cases; the second, growth rate for total deaths.
• The metric used is doubling time, by which I mean how quickly total cases or deaths are doubling.
• The plots track that doubling time at each date for our geographies. Darker colors reflect shorter doubling times, and thus periods of faster growth.
  • You can use the plots to track each geography over time and to compare the geographies to one another.
  • You can also compare the cases and death charts, to see how faster periods of death growth follow faster periods of case growth.

Case growth rates

• This section charts the growth rate of both total and new cases for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total cases and new cases.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of cases, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new cases) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new case reporting at the state level.
  • For total cases, the trendlines are a rolling 3-day average of daily growth rates in total cases. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total cases have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total case line get as close to zero as possible.
  • For new cases, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new cases. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total cases, we want to watch for the lines for new cases to get consistently below zero and stay there. That means that we are consistently seeing fewer new cases on a daily basis.

U.S.

Our states

Death growth rates

• This section charts the growth rate of both total and new deaths for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total deaths and new deaths.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of deaths, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new deaths) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new death reporting at the state level.
  • For total deaths, the trendlines are a rolling 3-day average of daily growth rates in total deaths. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total deaths have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total death line get as close to zero as possible.
  • For new deaths, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new deaths. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total deaths, we want to watch for the lines for new deaths to get consistently below zero and stay there. That means that we are consistently seeing fewer new deaths on a daily basis.

U.S.

Our states

By population rankings

This section tracks metrics for states and counties normalized for population (number of cases or deaths per million residents), and then compares these figures both for our geographies and the country overall.

States

• This section shows tables ranking all 50 states for per populations rates of total cases, new cases, total deaths, and new deaths.
• For each metric, in addition to the tables, the trends for the top states are plotted over time.
  • We only plot the top ten states for each metric so that the plots aren’t too crowded. But you can view the full 50-state rankings in the tables.

Total confirmed cases

Table of total confirmed cases per million residents (all 50 states)

Ranking	State	Cases Per Million
1	Louisiana	22,338
2	Arizona	21,486
3	New York	21,341
4	New Jersey	20,295
5	Florida	18,731
6	Rhode Island	17,202
7	Mississippi	16,687
8	Massachusetts	16,682
9	District of Columbia	16,505
10	Alabama	15,501
11	South Carolina	15,267
12	Delaware	14,584
13	Georgia	13,914
14	Connecticut	13,680
15	Maryland	13,617
16	Illinois	13,445
17	Nevada	13,315
18	Texas	13,231
19	Iowa	13,132
20	Tennessee	12,690
21	Nebraska	12,496
22	Arkansas	12,343
23	Utah	11,560
24	California	11,214
25	North Carolina	10,420
26	Idaho	9,742
27	Virginia	9,649
28	South Dakota	9,269
29	Indiana	9,221
30	New Mexico	8,810
31	Minnesota	8,782
32	Wisconsin	8,741
33	Kansas	8,719
34	Pennsylvania	8,619
35	Michigan	8,535
36	Colorado	7,483
37	North Dakota	7,372
38	Oklahoma	7,358
39	Washington	6,996
40	Ohio	6,993
41	Missouri	6,716
42	Kentucky	5,960
43	New Hampshire	4,688
44	Puerto Rico	4,373
45	Wyoming	4,155
46	Oregon	3,834
47	Alaska	3,753
48	West Virginia	3,177
49	Montana	2,912
50	Maine	2,794
51	Vermont	2,219
52	Hawaii	1,077

New confirmed cases

Table of new cases per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Cases Per Million
1	Louisiana	512
2	Florida	504
3	Mississippi	463
4	Alabama	383
5	Georgia	372
6	Nevada	368
7	Arizona	350
8	South Carolina	341
9	Tennessee	324
10	Texas	322
11	Arkansas	286
12	Idaho	282
13	California	276
14	Oklahoma	235
15	Missouri	215
16	Kansas	205
17	Utah	205
18	North Carolina	195
19	Iowa	180
20	North Dakota	178
21	Wisconsin	171
22	Nebraska	169
23	Delaware	156
24	New Mexico	152
25	Kentucky	148
26	Indiana	135
27	Ohio	129
28	Illinois	127
29	Maryland	122
30	Minnesota	120
31	Montana	116
32	Virginia	116
33	Colorado	112
34	Washington	110
35	Puerto Rico	107
36	District of Columbia	104
37	Alaska	101
38	Wyoming	96
39	West Virginia	92
40	Pennsylvania	75
41	Rhode Island	74
42	Oregon	72
43	Michigan	68
44	South Dakota	68
45	Connecticut	63
46	Massachusetts	46
47	New Jersey	38
48	New York	38
49	Hawaii	30
50	New Hampshire	27
51	Vermont	10
52	Maine	8

Total deaths

Table of total deaths per million residents (all 50 states)

Ranking	State	Deaths Per Million
1	New Jersey	1,774
2	New York	1,659
3	Connecticut	1,237
4	Massachusetts	1,232
5	Rhode Island	945
6	District of Columbia	823
7	Louisiana	799
8	Michigan	641
9	Illinois	598
10	Delaware	593
11	Maryland	566
12	Pennsylvania	559
13	Mississippi	491
14	Arizona	433
15	Indiana	428
16	Georgia	317
17	Colorado	311
18	New Hampshire	299
19	Alabama	293
20	New Mexico	286
21	Minnesota	284
22	Ohio	282
23	South Carolina	268
24	Florida	263
25	Iowa	261
26	Virginia	242
27	Nevada	234
28	California	210
29	Washington	208
30	Missouri	198
31	North Carolina	168
32	Texas	168
33	Nebraska	166
34	Kentucky	160
35	Wisconsin	152
36	South Dakota	137
37	North Dakota	135
38	Tennessee	135
39	Arkansas	130
40	Oklahoma	122
41	Kansas	113
42	Vermont	89
43	Maine	87
44	Utah	86
45	Idaho	82
46	Oregon	67
47	Puerto Rico	59
48	West Virginia	57
49	Montana	43
50	Wyoming	43
51	Alaska	23
52	Hawaii	17

New deaths

Table of new deaths per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Deaths Per Million
1	Delaware	18
2	Arizona	10
3	South Carolina	10
4	Alabama	9
5	Mississippi	8
6	Louisiana	7
7	Florida	6
8	Texas	6
9	Georgia	5
10	Nevada	4
11	California	3
12	Idaho	3
13	Tennessee	3
14	Arkansas	2
15	Massachusetts	2
16	Missouri	2
17	New Mexico	2
18	North Carolina	2
19	North Dakota	2
20	Ohio	2
21	Utah	2
22	Washington	2
23	Colorado	1
24	Illinois	1
25	Indiana	1
26	Iowa	1
27	Kansas	1
28	Kentucky	1
29	Maryland	1
30	Minnesota	1
31	Montana	1
32	Nebraska	1
33	New Hampshire	1
34	New Jersey	1
35	New York	1
36	Oklahoma	1
37	Oregon	1
38	Pennsylvania	1
39	Puerto Rico	1
40	Rhode Island	1
41	South Dakota	1
42	Wisconsin	1
43	Alaska	0
44	Connecticut	0
45	District of Columbia	0
46	Hawaii	0
47	Maine	0
48	Michigan	0
49	Vermont	0
50	Virginia	0
51	West Virginia	0
52	Wyoming	0

Counties

• This section focuses on the county level. It shows tables with our counties ranked by percentile of U.S. counties for per population rates of total cases and total deaths.
  • Each table also shows the top five counties in the country in addition to our counties, for added perspecive.
• In addition to the tables, our counties’ percentile for both total cases and total deaths are plotted over time.

Confirmed cases

Table showing total cases per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Cases Per Million	Raw Ranking	Percentile
Trousdale	Tennessee	138,249	1	99
Lake	Tennessee	100,342	2	99
Lee	Arkansas	98,114	3	99
Dakota	Nebraska	93,928	4	99
Buena Vista	Iowa	90,367	5	99
Davidson	Tennessee	27,550	117	96
Richland	South Carolina	16,464	372	88
Orange	California	10,504	803	74
York	South Carolina	9,702	884	71
Pierce	Washington	5,188	1563	50

Our county percentiles over time

Deaths

Table showing total deaths per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Deaths Per Million	Raw Ranking	Percentile
Hancock	Georgia	4,020	1	99
Randolph	Georgia	3,836	2	99
Terrell	Georgia	3,399	3	99
Early	Georgia	3,042	4	99
McKinley	New Mexico	2,929	5	99
Richland	South Carolina	298	632	79
Davidson	Tennessee	258	720	77
Orange	California	175	952	69
Pierce	Washington	145	1071	65
York	South Carolina	64	1611	48

Our county percentiles over time

Raw counts

• This section shows the raw counts over time for the U.S., our states, and our counties for four categories:
  • Total confirmed cases: This is the cumulative total of cases reported.
  • New confirmed cases: This is the number of new cases reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new cases, to help better visualize the peak.
  • Total deaths: This is the cumulative total of deaths reported.
  • New deaths: This is the number of new deaths reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new deaths, to help better visualize the peak.

Total confirmed cases

U.S.

Our states

Our counties

New confirmed cases

U.S.

Our states

Our counties

Total deaths

U.S.

Our states

Our counties

New deaths

U.S.

Our states

Our counties

Stay-at-home comparisons

• This section is a work in progress. It’s exploring what effect stay-at-home orders have on spread. It’s inherentely messy because some states have localized orders but not orders for their full state and because states implemented the orders at different times.
  • The first plot shows a rolling 3-day average of new cases per million residents, averaged across 2 groups of states – those with a state-wide stay-at-home order in effect and those without. The size of the dots reflect the number of states in each group for each given day (so the dots are getting bigger over time for the group with orders, as more states implement those orders).
    • So far, this view doesn’t show if these orders are “working” or not. Instead, the biggest takeaway from the current plot is that the states that implemented orders are reacting to having worse spread, which is why the case rate is so much higher at the start for them. As a state decides it has a problem and implements an order, it joins the grouping of states with orders and drives the rate up for that group.
    • Over time (if the policies work), what we’d expect is that the line for the group with orders flattens or declines in relation to the no-order group. It also may become moot if all states issue orders.
    • As states start rescending their orders, I’ll put them back into the no-order group, so the no-order group should beging to expand again.
  • The second plot tracks progress of states by the number of days since orders have been in effect. It includes only those states with orders and shows their average new case growth rate (a rolling 3-day average of daily growth rates) based on how long it’s been since the order went into effect. Again, the size of the dots show how many states are included in each point – i.e., as you go further right, the dots are smaller because less states are that far past their order.
    • I’m working on how to add a baseline to this chart to compare to states without orders, but that’s challenging because there’s no way to fully align timing.